Barbara Chapman is a Professor of Applied Mathematics and Statistics, and of Computer Science, at Stony Brook University, where she is affiliated with the Institute for Advanced Computational Science. She also directs Computer Science and Mathematics Research at Brookhaven National Laboratory.
Dr. Chapman has performed research on parallel programming interfaces and the related implementation technology for over 20 years and has moreover engaged in efforts to develop community standards for parallel programming, including OpenMP, OpenACC and OpenSHMEM. Her research group created the state-of-the-art OpenUH compiler that enabled practical experimentation with parallel language extensions and the corresponding implementation techniques. The group also created a reference implementation of the library-based OpenSHMEM programming interface. Dr. Chapman has co-authored over 200 papers and two books. She obtained a B.Sc. with 1st Class Honours in Mathematics from the University of Canterbury and a Ph.D. in Computer Science from Queen’s University of Belfast.
Machine Learning Needs HPC
Director, Computer Science and Mathematics
Brookhaven National Laboratory (BNL), New York, USA
Thursday 20 February 2020 – 3:30 pm
Machine Learning, especially Deep Learning, is deployed in increasingly sophisticated scenarios, enhancing traditional scientific computations, reducing the data storage needs of large-scale experiments, and processing data in situ from observations and experiments.
HPC expertise is exploring ways to improve the performance of AI without sacrificing accuracy. Can this help in our quest for convergence?